from weka.clusterers import Clusterer
import weka.core.converters as converters
data = converters.load_any_file("/home/ubuntu/test.csv")
data.class_is_last()
clusterer = Clusterer(classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
clusterer.build_clusterer(data)
print(clusterer)
# cluster the data
for inst in data:
cl = clusterer.cluster_instance(inst) # 0-based cluster index
dist = clusterer.distribution_for_instance(inst) # cluster membership distribution
print("cluster=" + str(cl) + ", distribution=" + str(dist))
I used the above code for doing k means custering i am not able to execute the program
The following are the errors I get
Traceback (most recent call last):
File "clus.py", line 6, in <module>
data = converters.load_any_file("/home/ubuntu/hello.csv")
File "/usr/local/lib/python2.7/dist-packages/weka/core/converters.py", line 255, in load_any_file
loader = loader_for_file(filename)
File "/usr/local/lib/python2.7/dist-packages/weka/core/converters.py", line 239, in loader_for_file
"(Ljava/lang/String;)Lweka/core/converters/AbstractFileLoader;", filename)
File "/usr/local/lib/python2.7/dist-packages/javabridge/jutil.py", line 932, in static_call
fn = make_static_call(class_name, method_name, sig)
File "/usr/local/lib/python2.7/dist-packages/javabridge/jutil.py", line 903, in make_static_call
klass = env.find_class(class_name)
AttributeError: 'NoneType' object has no attribute 'find_class'
I don't know why I am getting these errors. Can someone help me with this?
As described in the python-weka-wrapper API you have to import and start the Java Virtual Machine:
import weka.core.jvm as jvm
jvm.start()
It should solve your problem.
Related
I keep getting the below error when I try to access my model on hugging face spaces. I am building my model in a Kaggle notebook, then downloading to a pkl file to my spaces repo and git pushing to HF spaces. Below is my ImageDataLoaders class that I am using, as I suspect the error is coming from here.
dls = ImageDataLoaders.from_folder(path, valid_pct = 0.2, item_tfms=Resize(460), batch_tfms=aug_transforms(size=224, min_scale=0.75))
Here is the error I am getting.
Traceback (most recent call last):
File "app.py", line 5, in <module>
learn = load_learner('new_model.pkl')
File "/home/user/.local/lib/python3.8/site-packages/fastai/learner.py", line 428, in load_learner
try: res = torch.load(fname, map_location=map_loc, pickle_module=pickle_module)
File "/home/user/.local/lib/python3.8/site-packages/torch/serialization.py", line 712, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "/home/user/.local/lib/python3.8/site-packages/torch/serialization.py", line 1049, in _load
result = unpickler.load()
File "/home/user/.local/lib/python3.8/site-packages/torch/serialization.py", line 1042, in find_class
return super().find_class(mod_name, name)
AttributeError: Custom classes or functions exported with your `Learner` not available in namespace.\Re-declare/import before loading:
Can't get attribute 'Resampling' on <module 'PIL.Image' from '/home/user/.local/lib/python3.8/site-packages/PIL/Image.py'>
Here is my fill app.py code.
from fastai.vision.all import *
import gradio as gr
import skimage
learn = load_learner('new_model.pkl')
categories = ('deer', 'elk', 'moose')
def classify_image(img):
img = PILImage.create(img)
pred, idx, probs = learn.predict(img)
return dict(zip(categories, map(float,probs)))
image = gr.inputs.Image(type='pil', shape=(192,192))
label = gr.outputs.Label()
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label)
intf.launch(inline=False)
I am trying to run a simple example using sbmltoodepy and I am receiving the following error.
Traceback (most recent call last):
File "C:\Users\AppData\Roaming\JetBrains\PyCharm2021.2\scratches\scratch_79.py", line 8, in
ParseAndCreateModel(inputFilePath)#, jsonFilePath=None, outputFilePath=None)#, className="SBMLmodel")
File "C:\Users\AppData\Roaming\Python\Python39\site-packages\sbmltoodepy\utilities.py", line 66, in ParseAndCreateModel
modelData = ParseSBMLFile(inputFilePath)
File "C:\Users\AppData\Roaming\Python\Python39\site-packages\sbmltoodepy\parse.py", line 284, in ParseSBMLFile
assert(doc.getNumErrors() == 0)
AssertionError
Here is the code that I used.
import sbmltoodepy
from sbmltoodepy import ParseAndCreateModel
inputFilePath = "E:/Borisov2009_insulin_EGF.xml"
ParseAndCreateModel(inputFilePath, jsonFilePath=None, outputFilePath=None, className="SBMLmodel")
model = sbmltoodepy.SBMLmodel()
model.RunSimulation(1, absoluteTolerance = 1e-12, relativeTolerance = 1e-6)
Thank you in advance for your time.
I am trying to make a map in python using shapefiles I have downloaded from bbike.org. Here is my code:
import geopandas as gpd
import os
import sys
import matplotlib.pyplot as plt
bos_files_list = ['buildings.shx', 'landuse.shx', 'natural.shx', 'places.shx', 'points.shx', 'railways.shx', 'roads.shx']
cur_path = os.path.dirname(__file__)
def maps_of_bos(files):
for x in range(len(files)):
os.chdir(f'location/of/file')
f = open(f'{files[x]}', 'r')
gpd.read_file(f)
z = maps_of_bos(bos_files_list)
z.plot()
plt.show()
However, my error output is as follows:
Traceback (most recent call last):
File "test.py", line 16, in <module>
z = maps_of_bos(bos_files_list)
File "test.py", line 13, in maps_of_bos
gpd.read_file(f)
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/geopandas/io/f
ile.py", line 76, in read_file
with reader(path_or_bytes, **kwargs) as features:
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/contextlib.py", line 113, in
__enter__
return next(self.gen)
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/fiona/__init__
.py", line 206, in fp_reader
dataset = memfile.open()
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/fiona/io.py",
line 63, in open
return Collection(vsi_path, 'w', crs=crs, driver=driver,
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/fiona/collecti
on.py", line 126, in __init__
raise DriverError("no driver")
fiona.errors.DriverError: no driver
I am relatively new to python, and don't really understand my error. can someone please help me?
According to the docs read_file should take the path to the file not an object.
gpd.read_file(f'{files[x]}')
you dont need
f = open(f'{files[x]}', 'r')
I'm a beginner with myhdl.
I try to translate the following Verilog code to MyHDL:
module ModuleA(data_in, data_out, clk);
input data_in;
output reg data_out;
input clk;
always #(posedge clk) begin
data_out <= data_in;
end
endmodule
module ModuleB(data_in, data_out, clk);
input [1:0] data_in;
output [1:0] data_out;
input clk;
ModuleA instance1(data_in[0], data_out[0], clk);
ModuleA instance2(data_in[1], data_out[1], clk);
endmodule
Currently, I have this code:
import myhdl
#myhdl.block
def ModuleA(data_in, data_out, clk):
#myhdl.always(clk.posedge)
def logic():
data_out.next = data_in
return myhdl.instances()
#myhdl.block
def ModuleB(data_in, data_out, clk):
instance1 = ModuleA(data_in(0), data_out(0), clk)
instance2 = ModuleA(data_in(1), data_out(1), clk)
return myhdl.instances()
# Create signals
data_in = myhdl.Signal(myhdl.intbv()[2:])
data_out = myhdl.Signal(myhdl.intbv()[2:])
clk = myhdl.Signal(bool())
# Instantiate the DUT
dut = ModuleB(data_in, data_out, clk)
# Convert tfe DUT to Verilog
dut.convert()
But it doesn't works because signal slicing produce a read-only shadow signal (cf MEP-105).
So, what is it the good way in MyHDL to have a writable slice of a signal?
Edit:
This is the error I get
$ python demo.py
Traceback (most recent call last):
File "demo.py", line 29, in <module>
dut.convert()
File "/home/killruana/.local/share/virtualenvs/myhdl_sandbox-dYpBu4o5/lib/python3.6/site-packages/myhdl-0.10-py3.6.egg/myhdl/_block.py", line 342, in convert
File "/home/killruana/.local/share/virtualenvs/myhdl_sandbox-dYpBu4o5/lib/python3.6/site-packages/myhdl-0.10-py3.6.egg/myhdl/conversion/_toVerilog.py", line 177, in __call__
File "/home/killruana/.local/share/virtualenvs/myhdl_sandbox-dYpBu4o5/lib/python3.6/site-packages/myhdl-0.10-py3.6.egg/myhdl/conversion/_analyze.py", line 170, in _analyzeGens
File "/usr/lib/python3.6/ast.py", line 253, in visit
return visitor(node)
File "/home/killruana/.local/share/virtualenvs/myhdl_sandbox-dYpBu4o5/lib/python3.6/site-packages/myhdl-0.10-py3.6.egg/myhdl/conversion/_analyze.py", line 1072, in visit_Module
File "/home/killruana/.local/share/virtualenvs/myhdl_sandbox-dYpBu4o5/lib/python3.6/site-packages/myhdl-0.10-py3.6.egg/myhdl/conversion/_misc.py", line 148, in raiseError
myhdl.ConversionError: in file demo.py, line 4:
Signal has multiple drivers: data_out
You can use an intermediate list of Signal(bool()) as placeholder.
#myhdl.block
def ModuleB(data_in, data_out, clk):
tsig = [myhdl.Signal(bool(0)) for _ in range(len(data_in))]
instances = []
for i in range(len(data_in)):
instances.append(ModuleA(data_in(i), tsig[i], clk))
#myhdl.always_comb
def assign():
for i in range(len(data_out)):
data_out.next[i] = tsig[i]
return myhdl.instances()
A quick (probably non-fulfilling) comment, is that the intbv is treated as a single entity that can't have multiple drives. Two references that might help shed some light:
http://jandecaluwe.com/hdldesign/counting.html
http://docs.myhdl.org/en/stable/manual/structure.html#converting-between-lists-of-signals-and-bit-vectors
I am trying to create different python file where the code is given below. While calling the method, I pass the mydata as data frame with these columns
['wage', 'educ', 'exper', 'tenure'].
import pandas as pd
import numpy as np
from prettytable import PrettyTable as pt
def LinearRegressionOLS(mydata,target_column):
if(not isinstance(mydata,pd.DataFrame)):
raise TypeError("Data must be of type Data Frame")
if(not isinstance(target_column,str)):
raise TypeError("target_column must be String")
if(target_column not in mydata.columns):
raise KeyError("target_column doesn't exist in Data Frame")
data=mydata.copy()
data["one"]=np.ones(data.count()[target_column])
column_list=["one"]
for i in data.columns:
column_list.append(i)
Y=data[target_column].as_matrix()
data.drop(target_column,inplace=True,axis=1)
X=data[column_list].as_matrix()
del data
beta = np.matmul(np.matmul(np.linalg.inv(np.matmul(X.T,X)),X.T),Y)
predY = np.matmul(X,beta)
total = np.matmul((Y-np.mean(Y)).T,(Y-np.mean(Y)))
residual = np.matmul((Y-predY).T,(Y-predY))
sigma = np.matmul((Y-predY).T,(Y-predY))/(X.shape[0]-X.shape[1])
omega = np.square(sigma)*np.linalg.inv(np.matmul(X.T,X))
SE = np.sqrt(np.diag(omega))
tstat = beta/SE
Rsq = 1-(residual/total)
final = pt()
final.add_column(" ",column_list)
final.add_column("Coefficients",beta)
final.add_column("Standard Error",SE)
final.add_column("t-stat",tstat)
print(final)
print("Residual: ",residual)
print("Total: ",total)
print("Standard Error: ",sigma)
print("R Square: ",Rsq)
After running the above code, by calling the function given below,
>>> c
['wage', 'educ', 'exper', 'tenure']
>>> import LR_OLS as inf
>>> inf.LinearRegressionOLS(file[c],"wage")
, i get some error like this
Traceback (most recent call last):
File "<pyshell#182>", line 1, in <module>
inf.LinearRegressionOLS(file[c],"wage")
File "E:\python\LR_OLS.py", line 29, in LinearRegressionOLS
File "C:\Program Files\Python35\lib\site-packages\pandas\core\frame.py", line 2133, in __getitem__
return self._getitem_array(key)
File "C:\Program Files\Python35\lib\site-packages\pandas\core\frame.py", line 2177, in _getitem_array
indexer = self.loc._convert_to_indexer(key, axis=1)
File "C:\Program Files\Python35\lib\site-packages\pandas\core\indexing.py", line 1269, in _convert_to_indexer
.format(mask=objarr[mask]))
KeyError: "['wage'] not in index"
Can anyone help me as to why i am getting this error. How can i resolve it?
The problem is that you still have 'wage' in 'column_list. So in order to never let it get in there do the following adaptation:
for i in data.columns:
if i != 'wage': # add this line to your code
column_list.append(i)